Data Analyst Resume
Updated 29 August 2026 · written against live data analyst postings on JobCues
What does an ATS look for on a data analyst resume?
A data analyst resume is screened on SQL first, a business intelligence tool second, and a statistics vocabulary third. The bullet that separates a strong analyst resume from a weak one names the decision the analysis changed, not the dashboard it produced.
How an ATS reads this resume
SQL appears in almost every analyst posting and is matched literally. Naming BigQuery or Snowflake without the string SQL is the most common avoidable failure on this resume.
A dashboard is an output, not an outcome. Analyst resumes stall because every bullet ends at delivery; the ones that convert end at the decision, the change, or the money.
Domain vocabulary matters more here than in engineering. Retention, churn, cohort, funnel, attribution, and forecast accuracy are matched terms and they signal that you have worked with a business rather than a dataset.
Data Analyst resume keywords
These are the terms that recur across data analyst postings. A scanner matches them as literal strings, so spelling and casing carry more weight than they should. Only claim what you can defend.
Technical terms
Working-practice terms
Parent terms a scanner never infers
A keyword scanner matches letters. It does not know that one of these implies the other, so a resume that names only the left column fails a posting written with the right one. Writing both is the cheapest coverage gain available.
| You wrote | The posting asks for |
|---|---|
| BigQuery | SQL |
| pandas | Python |
| Tableau | data visualisation |
| dbt | data modelling |
| chi-squared test | statistical analysis |
Before and after bullets
Before
Built dashboards in Tableau for the sales team.
After
Built a Tableau pipeline-health dashboard on a 40-table SQL model that surfaced a stalled mid-funnel stage; the resulting process change lifted quarter-over-quarter close rate by 6 points.
Ends at the decision and its result rather than at the dashboard, and picks up SQL, Tableau, and funnel language on the way.
Before
Analysed customer churn.
After
Ran a cohort analysis in Python and pandas across 18 months of subscriptions, isolating a 3x churn spike in accounts that never used the import tool and driving an onboarding change that cut 90-day churn from 11% to 7%.
Names the method, the window, the finding, and the effect. Every one of those is a keyword and a credibility signal.
Before
Automated weekly reporting.
After
Replaced a manual weekly Excel report with a dbt model and a scheduled Looker delivery, returning about 6 hours a week to the analytics team and removing three recurring copy-paste errors.
Time saved is the analyst metric that is always available, and the error count makes the automation claim concrete.
Section order
Experience leads. A fresh quantitative degree is worth highlighting inside the Education entry with a details line naming the relevant coursework — not by moving Education up the page. Projects earn their place when the work history is short or confidential; cut them once two Experience bullets say the same thing better.
Summary is optional. Use it to clear a hard requirement the posting states: work authorisation, a named language level (JLPT N2, IELTS 7), security clearance, willingness to relocate, a required licence, or a notice period. If you want to add one anyway, keep it to one line about you or your work, then anything that genuinely catches a recruiter's eye. But we recommend putting that energy into the first few sections instead and making those count.
Common mistakes
Ending the bullet at the deliverable
Built a dashboard, produced a report, ran an analysis. Each one stops one clause short of the thing that gets you hired: what changed.
Naming the warehouse but not SQL
Snowflake and BigQuery are not the string SQL. Both belong on the page.
Listing Excel as an afterthought
Excel is still named in a large share of analyst postings and is matched literally. If you use it, write it, including the specific functions if the posting names them.
Frequently asked questions
Do I need Python on a data analyst resume?
Not universally. SQL and a BI tool clear most analyst postings; Python widens the range and is close to required for anything labelled analytics engineer or senior analyst. Add it only if you can actually use pandas under questioning.
How do I show impact when I only support other teams?
Attribute the decision, not the revenue. 'Analysis that led the team to X, which moved Y' is honest, checkable, and reads stronger than claiming the outcome as yours.
Should a data analyst resume include a portfolio?
A short one helps when your work history is thin or confidential. Two well-documented analyses with the question, the method, and the conclusion beat ten notebooks.
Run this against a real data analyst posting
The lists above are the general case. Every posting has its own keyword set, and the only score that matters is the one against the job you are applying to. Paste the description and get the matched terms, the missing terms, and a rewritten resume in about 15 seconds.